Abstract
Watching Internet videos has definitely become one of the top activities of PC users, and with the advent and rapid adoption of smart phones globally, promises to be one of the top activities for mobile device users as well. Hence, it is timely to investigate whether it may be possible to estimate the preferences and intent of users from the Internet videos they watch, since such knowledge can enable service providers to offer personalized services, like relevant video advertisements and video recommendations. Such knowledge can also be leveraged to reduce congestion in networks, through improved video caching techniques. In this work, we study the effect of different webpage (such as YouTube) categorization techniques on the distribution of user-interests across the various categories.